Estimation of Distribution Algorithm Based on Sequential Importance Sampling Particle Filters

ZHANG Jian-hua; ZENG Jian-chao

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Estimation of Distribution Algorithm Based on Sequential Importance Sampling Particle Filters

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- Estimation of Distribution Algorithm Based on Sequential Importance Sampling Particle Filters
- Acta Electronica Sinica Vol. 38, Issue 12, Pages: 2929-2932(2010)
- 作者机构：
  
  1. 兰州理工大学电气工程与信息工程学院,甘肃,兰州,730050
  2. 太原科技大学复杂系统与智能计算实验室,山西,太原,030024
  3. 中北大学电子与计算机科学技术学院,山西,太原,030051
  4. 兰州理工大学电气工程与信息工程学院甘肃兰州,730050
  5. 太原科技大学复杂系统与智能计算实验室山西太原,030024
  6. 中北大学电子与计算机科学技术学院山西太原,030051
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP301.6
- Published：2010
- 稿件说明：
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ZHANG Jian-hua, ZENG Jian-chao. Estimation of Distribution Algorithm Based on Sequential Importance Sampling Particle Filters[J]. Acta Electronica Sinica, 2010, 38(12): 2929-2932.
DOI：

ZHANG Jian-hua, ZENG Jian-chao. Estimation of Distribution Algorithm Based on Sequential Importance Sampling Particle Filters[J]. Acta Electronica Sinica, 2010, 38(12): 2929-2932. DOI：

摘要

连续域分布估计算法一般假设数据服从高斯分布

而且大多采用了单峰的概率模型

但是对于一些复杂的优化问题

单峰的高斯分布模型不能有效地描述解在空间的分布.本文提出一种基于序贯重点采样粒子滤波的分布估计算法

采用带权粒子描述优选集样本服从的概率分布并从中采样得到下一代种群

不需要假设样本服从高斯分布

并且算法采用的概率模型是多峰的.仿真实验结果验证了本文方法的正确性和有效性.

Abstract

Estimation of distribution algorithm in continuous domains is generally based on assumption that variables subject to Gaussian distribution and that the probability model is single-peaked one

which is not capable of describing the solutions distribution effectively for complex optimization problems.Aiming to improve such drawback

an estimation of distribution algorithm depending upon sequential importance sampling particle filters is presented.In this algorithm

the variables are not required to subject to Gaussian distribution.Instead

the distribution of samples is represented by weighted particles and the used probability model is multi-peaked.The next generation of population is produced from the above distribution.The simulation results indicate the validity of the algorithm.

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